# import torch
# import torch.nn as nn
# try:
#     from torch.hub import load_state_dict_from_url
# except ImportError:
#     from torch.utils.model_zoo import load_url as load_state_dict_from_url
# from ..registry import BACKBONES
# model_urls = {
#     'resnext101_32x8d': 'https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth',
#     'resnext101_32x16d': 'https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth',
#     'resnext101_32x32d': 'https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth',
#     'resnext101_32x48d': 'https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth',
# }
#
# def _resnext(arch, block, layers, pretrained, progress, **kwargs):
#     model = ResNet(block, layers, **kwargs)
#     if pretrained:
#         # from collections import OrderedDict
#         state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
#         new_state_dict = model.state_dict()
#         for k, v in state_dict.items():
#             if 'fc' not in k:
#                 new_state_dict[k] = v
#         # new_state_dict.update(state_dict)
#         model.load_state_dict(new_state_dict)
#         print("Load pretrained model successfully!!!")
#     return model
#
#
# class ResNet(nn.Module):
#
#     def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
#                  groups=1, width_per_group=64, replace_stride_with_dilation=None,
#                  norm_layer=None):
#         super(ResNet, self).__init__()
#         if norm_layer is None:
#             norm_layer = nn.BatchNorm2d
#         self._norm_layer = norm_layer
#
#         self.inplanes = 64
#         self.dilation = 1
#         if replace_stride_with_dilation is None:
#             # each element in the tuple indicates if we should replace
#             # the 2x2 stride with a dilated convolution instead
#             replace_stride_with_dilation = [False, False, False]
#         if len(replace_stride_with_dilation) != 3:
#             raise ValueError("replace_stride_with_dilation should be None "
#                              "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
#         self.groups = groups
#         self.base_width = width_per_group
#         self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
#                                bias=False)
#         self.bn1 = norm_layer(self.inplanes)
#         self.relu = nn.ReLU(inplace=True)
#
#         self.ca = ChannelAttention(self.inplanes)
#         self.sa = SpatialAttention()
#
#         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
#         self.layer1 = self._make_layer(block, 64, layers[0])
#         self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
#                                        dilate=replace_stride_with_dilation[0])
#         self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
#                                        dilate=replace_stride_with_dilation[1])
#         self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
#                                        dilate=replace_stride_with_dilation[2])
#         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
#         self.fc = nn.Linear(512 * block.expansion, num_classes)
#
#         for m in self.modules():
#             if isinstance(m, nn.Conv2d):
#                 nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
#             elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
#                 nn.init.constant_(m.weight, 1)
#                 nn.init.constant_(m.bias, 0)
#
#         # Zero-initialize the last BN in each residual branch,
#         # so that the residual branch starts with zeros, and each residual block behaves like an identity.
#         # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
#         if zero_init_residual:
#             for m in self.modules():
#                 if isinstance(m, Bottleneck):
#                     nn.init.constant_(m.bn3.weight, 0)
#                 elif isinstance(m, BasicBlock):
#                     nn.init.constant_(m.bn2.weight, 0)
#
#     def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
#         norm_layer = self._norm_layer
#         downsample = None
#         previous_dilation = self.dilation
#         if dilate:
#             self.dilation *= stride
#             stride = 1
#         if stride != 1 or self.inplanes != planes * block.expansion:
#             downsample = nn.Sequential(
#                 conv1x1(self.inplanes, planes * block.expansion, stride),
#                 norm_layer(planes * block.expansion),
#             )
#
#         layers = []
#         layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
#                             self.base_width, previous_dilation, norm_layer))
#         self.inplanes = planes * block.expansion
#         for _ in range(1, blocks):
#             layers.append(block(self.inplanes, planes, groups=self.groups,
#                                 base_width=self.base_width, dilation=self.dilation,
#                                 norm_layer=norm_layer))
#
#         return nn.Sequential(*layers)
#
#     def forward(self, x):
#         x = self.conv1(x)
#         x = self.bn1(x)
#         x = self.relu(x)
#         x = self.ca(x) * x
#         x = self.sa(x) * x
#         x = self.maxpool(x)
#
#         x = self.layer1(x)
#         x = self.layer2(x)
#         x = self.layer3(x)
#         x = self.layer4(x)
#
#         x = self.avgpool(x)
#         x = x.reshape(x.size(0), -1)
#         x = self.fc(x)
#
#         return x
#
# class BasicBlock(nn.Module):
#     expansion = 1
#
#     def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
#                  base_width=64, dilation=1, norm_layer=None):
#         super(BasicBlock, self).__init__()
#         if norm_layer is None:
#             norm_layer = nn.BatchNorm2d
#         if groups != 1 or base_width != 64:
#             raise ValueError('BasicBlock only supports groups=1 and base_width=64')
#         if dilation > 1:
#             raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
#         # Both self.conv1 and self.downsample layers downsample the input when stride != 1
#         self.conv1 = conv3x3(inplanes, planes, stride)
#         self.bn1 = norm_layer(planes)
#         self.relu = nn.ReLU(inplace=True)
#         self.conv2 = conv3x3(planes, planes)
#         self.bn2 = norm_layer(planes)
#
#         self.downsample = downsample
#         self.stride = stride
#
#     def forward(self, x):
#         identity = x
#
#         out = self.conv1(x)
#         out = self.bn1(out)
#         out = self.relu(out)
#
#         out = self.conv2(out)
#         out = self.bn2(out)
#
#         if self.downsample is not None:
#             identity = self.downsample(x)
#
#         out += identity
#         out = self.relu(out)
#
#         return out
#
# class Bottleneck(nn.Module):
#     expansion = 4
#
#     def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
#                  base_width=64, dilation=1, norm_layer=None):
#         super(Bottleneck, self).__init__()
#         if norm_layer is None:
#             norm_layer = nn.BatchNorm2d
#         width = int(planes * (base_width / 64.)) * groups
#         # Both self.conv2 and self.downsample layers downsample the input when stride != 1
#         self.conv1 = conv1x1(inplanes, width)
#         self.bn1 = norm_layer(width)
#         self.conv2 = conv3x3(width, width, stride, groups, dilation)
#         self.bn2 = norm_layer(width)
#         self.conv3 = conv1x1(width, planes * self.expansion)
#         self.bn3 = norm_layer(planes * self.expansion)
#         self.relu = nn.ReLU(inplace=True)
#
#         self.downsample = downsample
#         self.stride = stride
#
#     def forward(self, x):
#         identity = x
#
#         out = self.conv1(x)
#         out = self.bn1(out)
#         out = self.relu(out)
#
#         out = self.conv2(out)
#         out = self.bn2(out)
#         out = self.relu(out)
#
#         out = self.conv3(out)
#         out = self.bn3(out)
#
#         if self.downsample is not None:
#             identity = self.downsample(x)
#
#         out += identity
#         out = self.relu(out)
#
#         return out
#
#
#
# def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
#     """3x3 convolution with padding"""
#     return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
#                      padding=dilation, groups=groups, bias=False, dilation=dilation)
#
#
# def conv1x1(in_planes, out_planes, stride=1):
#     """1x1 convolution"""
#     return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
#
#
#
# class ChannelAttention(nn.Module):
#     def __init__(self, in_planes, ratio=16):
#         super(ChannelAttention, self).__init__()
#         self.avg_pool = nn.AdaptiveAvgPool2d(1)
#         self.max_pool = nn.AdaptiveMaxPool2d(1)
#
#         self.fc1   = nn.Conv2d(in_planes, in_planes // 16, 1, bias=False)
#         self.relu1 = nn.ReLU()
#         self.fc2   = nn.Conv2d(in_planes // 16, in_planes, 1, bias=False)
#
#         self.sigmoid = nn.Sigmoid()
#
#     def forward(self, x):
#         avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
#         max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
#         out = avg_out + max_out
#         return self.sigmoid(out)
#
# class SpatialAttention(nn.Module):
#     def __init__(self, kernel_size=7):
#         super(SpatialAttention, self).__init__()
#
#         assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
#         padding = 3 if kernel_size == 7 else 1
#
#         self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
#         self.sigmoid = nn.Sigmoid()
#
#     def forward(self, x):
#         avg_out = torch.mean(x, dim=1, keepdim=True)
#         max_out, _ = torch.max(x, dim=1, keepdim=True)
#         x = torch.cat([avg_out, max_out], dim=1)
#         x = self.conv1(x)
#         return self.sigmoid(x)
#
#
# @BACKBONES.register_module
# class resnext101_32x8d_wsl(ResNet):
#
#     def __init__(self, block=Bottleneck, layers=[3, 4, 23, 3], num_classes=2, goups=32, width_per_group=8):
#         # block = Bottleneck
#         # layers = [3, 4, 23, 3]
#         # groups = 32
#         # width_per_group = 8
#         super(resnext101_32x8d_wsl, self).__init__(block=block, layers=layers, num_classes=num_classes, groups=goups, width_per_group=width_per_group)
#         pass
#
#     def init_weights(self, pretrained=None):
#         state_dict = load_state_dict_from_url(model_urls['resnext101_32x8d'], progress=True)
#         new_state_dict = self.state_dict()
#         for k, v in state_dict.items():
#             if 'fc' not in k:
#                 new_state_dict[k] = v
#         # new_state_dict.update(state_dict)
#         self.load_state_dict(new_state_dict)
#         print("Load pretrained model successfully!!!")
#
# @BACKBONES.register_module
# class resnext101_32x16d_wsl(ResNet):
#
#     def __init__(self, block=Bottleneck, layers=[3, 4, 23, 3], num_classes=2, goups=32, width_per_group=16):
#         # block = Bottleneck
#         # layers = [3, 4, 23, 3]
#         # groups = 32
#         # width_per_group = 8
#         super(resnext101_32x16d_wsl, self).__init__(block=block, layers=layers, num_classes=num_classes, groups=goups, width_per_group=width_per_group)
#         pass
#
#     def init_weights(self, pretrained=None):
#         state_dict = load_state_dict_from_url(model_urls['resnext101_32x16d'], progress=True)
#         new_state_dict = self.state_dict()
#         for k, v in state_dict.items():
#             if 'fc' not in k:
#                 new_state_dict[k] = v
#         # new_state_dict.update(state_dict)
#         self.load_state_dict(new_state_dict)
#         print("Load pretrained model successfully!!!")
#
# @BACKBONES.register_module
# class resnext101_32x32d_wsl(ResNet):
#
#     def __init__(self, block=Bottleneck, layers=[3, 4, 23, 3], num_classes=2, goups=32, width_per_group=32):
#         # block = Bottleneck
#         # layers = [3, 4, 23, 3]
#         # groups = 32
#         # width_per_group = 8
#         super(resnext101_32x32d_wsl, self).__init__(block=block, layers=layers, num_classes=num_classes, groups=goups, width_per_group=width_per_group)
#         pass
#
#     def init_weights(self, pretrained=None):
#         state_dict = load_state_dict_from_url(model_urls['resnext101_32x32d'], progress=True)
#         new_state_dict = self.state_dict()
#         for k, v in state_dict.items():
#             if 'fc' not in k:
#                 new_state_dict[k] = v
#         # new_state_dict.update(state_dict)
#         self.load_state_dict(new_state_dict)
#         print("Load pretrained model successfully!!!")
#
# @BACKBONES.register_module
# class resnext101_32x48d_wsl(ResNet):
#
#     def __init__(self, block=Bottleneck, layers=[3, 4, 23, 3], num_classes=2, goups=32, width_per_group=48):
#         # block = Bottleneck
#         # layers = [3, 4, 23, 3]
#         # groups = 32
#         # width_per_group = 8
#         super(resnext101_32x48d_wsl, self).__init__(block=block, layers=layers, num_classes=num_classes, groups=goups, width_per_group=width_per_group)
#         pass
#
#     def init_weights(self, pretrained=None):
#         state_dict = load_state_dict_from_url(model_urls['resnext101_32x48d'], progress=True)
#         new_state_dict = self.state_dict()
#         for k, v in state_dict.items():
#             if 'fc' not in k:
#                 new_state_dict[k] = v
#         # new_state_dict.update(state_dict)
#         self.load_state_dict(new_state_dict)
#         print("Load pretrained model successfully!!!")
#
# # def resnext101_32x8d_wsl(progress=True, pretrained=False, **kwargs):
# #     """Constructs a ResNeXt-101 32x8 model pre-trained on weakly-supervised data
# #     and finetuned on ImageNet from Figure 5 in
# #     `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_
# #     Args:
# #         progress (bool): If True, displays a progress bar of the download to stderr.
# #     """
# #     kwargs['groups'] = 32
# #     kwargs['width_per_group'] = 8
# #     return _resnext('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)
# #
# #
# # def resnext101_32x16d_wsl(progress=True, pretrained=False, **kwargs):
# #     """Constructs a ResNeXt-101 32x16 model pre-trained on weakly-supervised data
# #     and finetuned on ImageNet from Figure 5 in
# #     `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_
# #     Args:zz
# #         progress (bool): If True, displays a progress bar of the download to stderr.
# #     """
# #     kwargs['groups'] = 32
# #     kwargs['width_per_group'] = 16
# #     return _resnext('resnext101_32x16d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)
# #
# #
# # def resnext101_32x32d_wsl(progress=True, pretrained=False, **kwargs):
# #     """Constructs a ResNeXt-101 32x32 model pre-trained on weakly-supervised data
# #     and finetuned on ImageNet from Figure 5 in
# #     `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_
# #     Args:
# #         progress (bool): If True, displays a progress bar of the download to stderr.
# #     """
# #     kwargs['groups'] = 32
# #     kwargs['width_per_group'] = 32
# #     return _resnext('resnext101_32x32d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)
# #
# #
# # def resnext101_32x48d_wsl(progress=True, pretrained=False, **kwargs):
# #     """Constructs a ResNeXt-101 32x48 model pre-trained on weakly-supervised data
# #     and finetuned on ImageNet from Figure 5 in
# #     `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_
# #     Args:
# #         progress (bool): If True, displays a progress bar of the download to stderr.
# #     """
# #     kwargs['groups'] = 32
# #     kwargs['width_per_group'] = 48
# #     return _resnext('resnext101_32x48d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)
#
